Boden
Benefits of Our Transformation
Reducing Customer Churn with Machine Learning and
Data Driven Behavioural Insights
About Boden
Boden is a leading British clothing retailer with a strong online presence and loyal customer base. Known for its distinctive style and commitment to quality, Boden has successfully built a digital-first business model. However, as the online retail market became increasingly competitive, the company faced the challenge of high customer churn, with many online shoppers failing to return after their initial purchase.
Results That
Matter
£9.6m
Revenue Increase
£149m identified
Potential Net Revenue
Insight-driven retention
Customer Understanding
Predictive modelling at scale
Data Activation
The Opportunity
Boden recognised that long-term success depended not only on attracting new shoppers but also on keeping existing ones engaged. To address declining repeat purchase rates, the business set out to identify customers most at risk of leaving and understand the behavioural patterns driving churn.
The focus was to move beyond basic demographic profiling toward behavioural and predictive analytics, allowing the team to take proactive, personalised action. The ultimate goal was to increase customer retention and lifetime value by embedding data-driven intelligence into marketing, product, and customer engagement strategies.
Solutions Delivered
Beyond partnered with Boden to develop a data-driven churn-reduction framework combining behavioural insight and machine learning.
- Machine Learning Modelling: Tested over 40 machine-learning models using historical, behavioural, and predictive variables to identify the strongest drivers of churn. These models enabled precise identification of at-risk segments and informed proactive retention campaigns.
Seasonal and Behavioural Profiling: Analysed seasonal spending and key behavioural traits — such as browsing frequency and item return habits — to determine when and why customers were most likely to leave.
Churn-Prediction Model: Built a predictive model that pinpointed patterns linked to churn, such as optimal page-view ranges (10–30), ideal price ranges (£20–£60), and risk factors like returning too few or too many items.
Results & Impact
The analysis allowed Boden to implement targeted retention strategies that significantly reduced churn and improved customer loyalty.
- £9.6 million in annual revenue growth was achieved directly through reduced churn.
- £149 million in additional potential net revenue opportunities were identified for future realisation.
By embedding these insights into its marketing and product planning, Boden was able to engage customers more effectively and secure long-term revenue growth through improved loyalty.
Lesson Learned
Boden’s experience demonstrates how advanced analytics and behavioural insight can transform customer retention. Machine-learning models provided clear evidence of what drives loyalty, while behavioural profiling translated complex data into actionable strategy. The project proved that tackling churn isn’t just about targeting lapsing customers — it’s about understanding the underlying behaviours that influence their decisions and using that insight to create more personalised, rewarding experiences.
Beyond helped us unlock the full potential of our customer data, giving us a much clearer view of who our customers are and how to engage them. Their expertise turned analytics into action, enabling truly personalised marketing that feels authentic to the Boden brand.
Head of Customer Marketing, Boden
